Overview

Dataset statistics

Number of variables12
Number of observations252
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.8 KiB
Average record size in memory283.6 B

Variable types

Text2
Categorical2
Numeric8

Alerts

processed_date has constant value "2025-10-24T01:07:19.903300"Constant
weather_data_points has constant value "1989"Constant
avg_humidity_2m is highly overall correlated with avg_weather_code and 1 other fieldsHigh correlation
avg_temperature_2m is highly overall correlated with avg_wind_direction_10m and 1 other fieldsHigh correlation
avg_weather_code is highly overall correlated with avg_humidity_2m and 1 other fieldsHigh correlation
avg_wind_direction_10m is highly overall correlated with avg_temperature_2m and 1 other fieldsHigh correlation
latitude is highly overall correlated with avg_temperature_2m and 1 other fieldsHigh correlation
total_precipitation is highly overall correlated with avg_humidity_2m and 1 other fieldsHigh correlation
cfb_school has unique valuesUnique

Reproduction

Analysis started2025-12-08 06:35:01.548600
Analysis finished2025-12-08 06:35:03.680809
Duration2.13 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

cfb_school
Text

Unique 

Distinct252
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2025-12-07T22:35:03.746159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length18
Mean length10.904762
Min length3

Characters and Unicode

Total characters2748
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique252 ?
Unique (%)100.0%

Sample

1st rowAbilene Christian
2nd rowAir Force
3rd rowAkron
4th rowAlabama
5th rowAlabama A&M
ValueCountFrequency (%)
state59
 
14.3%
carolina8
 
1.9%
texas8
 
1.9%
north7
 
1.7%
southern7
 
1.7%
illinois6
 
1.5%
tech6
 
1.5%
michigan5
 
1.2%
south5
 
1.2%
alabama5
 
1.2%
Other values (216)296
71.8%
2025-12-07T22:35:03.853676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a296
 
10.8%
e251
 
9.1%
t243
 
8.8%
n183
 
6.7%
o179
 
6.5%
160
 
5.8%
r155
 
5.6%
i152
 
5.5%
s132
 
4.8%
l100
 
3.6%
Other values (44)897
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a296
 
10.8%
e251
 
9.1%
t243
 
8.8%
n183
 
6.7%
o179
 
6.5%
160
 
5.8%
r155
 
5.6%
i152
 
5.5%
s132
 
4.8%
l100
 
3.6%
Other values (44)897
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a296
 
10.8%
e251
 
9.1%
t243
 
8.8%
n183
 
6.7%
o179
 
6.5%
160
 
5.8%
r155
 
5.6%
i152
 
5.5%
s132
 
4.8%
l100
 
3.6%
Other values (44)897
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a296
 
10.8%
e251
 
9.1%
t243
 
8.8%
n183
 
6.7%
o179
 
6.5%
160
 
5.8%
r155
 
5.6%
i152
 
5.5%
s132
 
4.8%
l100
 
3.6%
Other values (44)897
32.6%
Distinct249
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size18.9 KiB
2025-12-07T22:35:03.924990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length38
Mean length27.126984
Min length14

Characters and Unicode

Total characters6836
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)97.6%

Sample

1st rowAbilene Christian University
2nd rowUnited States Air Force Academy
3rd rowUniversity of Akron Main Campus
4th rowThe University of Alabama
5th rowAlabama A & M University
ValueCountFrequency (%)
university216
25.1%
of80
 
9.3%
state69
 
8.0%
campus18
 
2.1%
at15
 
1.7%
texas11
 
1.3%
carolina10
 
1.2%
college10
 
1.2%
the10
 
1.2%
southern9
 
1.0%
Other values (253)413
48.0%
2025-12-07T22:35:04.039638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i723
 
10.6%
609
 
8.9%
e580
 
8.5%
t568
 
8.3%
n504
 
7.4%
a442
 
6.5%
r433
 
6.3%
s425
 
6.2%
o330
 
4.8%
y268
 
3.9%
Other values (41)1954
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i723
 
10.6%
609
 
8.9%
e580
 
8.5%
t568
 
8.3%
n504
 
7.4%
a442
 
6.5%
r433
 
6.3%
s425
 
6.2%
o330
 
4.8%
y268
 
3.9%
Other values (41)1954
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i723
 
10.6%
609
 
8.9%
e580
 
8.5%
t568
 
8.3%
n504
 
7.4%
a442
 
6.5%
r433
 
6.3%
s425
 
6.2%
o330
 
4.8%
y268
 
3.9%
Other values (41)1954
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i723
 
10.6%
609
 
8.9%
e580
 
8.5%
t568
 
8.3%
n504
 
7.4%
a442
 
6.5%
r433
 
6.3%
s425
 
6.2%
o330
 
4.8%
y268
 
3.9%
Other values (41)1954
28.6%

processed_date
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2025-10-24T01:07:19.903300
252 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters6552
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-10-24T01:07:19.903300
2nd row2025-10-24T01:07:19.903300
3rd row2025-10-24T01:07:19.903300
4th row2025-10-24T01:07:19.903300
5th row2025-10-24T01:07:19.903300

Common Values

ValueCountFrequency (%)
2025-10-24T01:07:19.903300252
100.0%

Length

2025-12-07T22:35:04.069795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:35:04.088153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025-10-24t01:07:19.903300252
100.0%

Most occurring characters

ValueCountFrequency (%)
01764
26.9%
2756
11.5%
1756
11.5%
-504
 
7.7%
:504
 
7.7%
9504
 
7.7%
3504
 
7.7%
5252
 
3.8%
4252
 
3.8%
T252
 
3.8%
Other values (2)504
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01764
26.9%
2756
11.5%
1756
11.5%
-504
 
7.7%
:504
 
7.7%
9504
 
7.7%
3504
 
7.7%
5252
 
3.8%
4252
 
3.8%
T252
 
3.8%
Other values (2)504
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01764
26.9%
2756
11.5%
1756
11.5%
-504
 
7.7%
:504
 
7.7%
9504
 
7.7%
3504
 
7.7%
5252
 
3.8%
4252
 
3.8%
T252
 
3.8%
Other values (2)504
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01764
26.9%
2756
11.5%
1756
11.5%
-504
 
7.7%
:504
 
7.7%
9504
 
7.7%
3504
 
7.7%
5252
 
3.8%
4252
 
3.8%
T252
 
3.8%
Other values (2)504
 
7.7%

latitude
Real number (ℝ)

High correlation 

Distinct249
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.035031
Minimum21.298598
Maximum47.921654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.113651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21.298598
5-th percentile29.71927
Q133.445569
median37.061357
Q340.72759
95-th percentile44.167699
Maximum47.921654
Range26.623056
Interquartile range (IQR)7.282021

Descriptive statistics

Standard deviation4.7447941
Coefficient of variation (CV)0.12811638
Kurtosis-0.34397669
Mean37.035031
Median Absolute Deviation (MAD)3.649119
Skewness-0.14412873
Sum9332.8278
Variance22.513071
MonotonicityNot monotonic
2025-12-07T22:35:04.151421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.6938192
 
0.8%
33.2437452
 
0.8%
32.775252
 
0.8%
32.4689431
 
0.4%
29.7178971
 
0.4%
40.4987691
 
0.4%
40.5204151
 
0.4%
37.5773931
 
0.4%
41.4846911
 
0.4%
32.8436121
 
0.4%
Other values (239)239
94.8%
ValueCountFrequency (%)
21.2985981
0.4%
25.721261
0.4%
25.757321
0.4%
26.3724211
0.4%
28.0614581
0.4%
28.6021591
0.4%
29.2071951
0.4%
29.467081
0.4%
29.5837091
0.4%
29.646291
0.4%
ValueCountFrequency (%)
47.9216541
0.4%
47.655381
0.4%
47.4906671
0.4%
46.8931271
0.4%
46.8593121
0.4%
46.7304481
0.4%
46.7274061
0.4%
45.6667261
0.4%
45.5116011
0.4%
44.9728511
0.4%

longitude
Real number (ℝ)

Distinct249
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.22382
Minimum-157.81898
Maximum-68.669332
Zeros0
Zeros (%)0.0%
Negative252
Negative (%)100.0%
Memory size2.1 KiB
2025-12-07T22:35:04.192029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-157.81898
5-th percentile-119.02825
Q1-96.397625
median-86.815822
Q3-80.183595
95-th percentile-73.18804
Maximum-68.669332
Range89.149647
Interquartile range (IQR)16.21403

Descriptive statistics

Standard deviation14.012458
Coefficient of variation (CV)-0.15530774
Kurtosis1.6779806
Mean-90.22382
Median Absolute Deviation (MAD)7.855728
Skewness-1.1752418
Sum-22736.403
Variance196.34898
MonotonicityNot monotonic
2025-12-07T22:35:04.227435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-95.5159542
 
0.8%
-95.9084512
 
0.8%
-117.0712282
 
0.8%
-99.7097971
 
0.4%
-95.4020351
 
0.4%
-74.4462511
 
0.4%
-80.2104681
 
0.4%
-77.5388061
 
0.4%
-71.5273561
 
0.4%
-96.7833651
 
0.4%
Other values (239)239
94.8%
ValueCountFrequency (%)
-157.8189791
0.4%
-123.2747231
0.4%
-123.0757921
0.4%
-122.6862891
0.4%
-122.305141
0.4%
-122.2604631
0.4%
-122.1673591
0.4%
-121.8806211
0.4%
-121.7495671
0.4%
-121.4235491
0.4%
ValueCountFrequency (%)
-68.6693321
0.4%
-70.9324651
0.4%
-71.0887821
0.4%
-71.1242161
0.4%
-71.1692421
0.4%
-71.5273561
0.4%
-71.5395471
0.4%
-71.8082141
0.4%
-72.2499481
0.4%
-72.5267281
0.4%

avg_temperature_2m
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.170112
Minimum9.9560583
Maximum26.966968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.261485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.9560583
5-th percentile12.377391
Q114.644495
median17.843866
Q321.232642
95-th percentile24.882303
Maximum26.966968
Range17.01091
Interquartile range (IQR)6.5881473

Descriptive statistics

Standard deviation4.0250654
Coefficient of variation (CV)0.22152122
Kurtosis-0.93748755
Mean18.170112
Median Absolute Deviation (MAD)3.241453
Skewness0.23335993
Sum4578.8682
Variance16.201151
MonotonicityNot monotonic
2025-12-07T22:35:04.302597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.882302662
 
0.8%
22.5023632
 
0.8%
20.629512322
 
0.8%
18.384715942
 
0.8%
23.441025641
 
0.4%
23.296933131
 
0.4%
16.060432381
 
0.4%
14.620160881
 
0.4%
17.750779291
 
0.4%
24.698944191
 
0.4%
Other values (238)238
94.4%
ValueCountFrequency (%)
9.9560583211
0.4%
10.37280041
0.4%
10.58607341
0.4%
10.598340871
0.4%
11.548466571
0.4%
11.616189041
0.4%
11.61970841
0.4%
11.630165911
0.4%
11.915937661
0.4%
11.918501761
0.4%
ValueCountFrequency (%)
26.966968331
0.4%
26.61583711
0.4%
26.569984921
0.4%
26.272398191
0.4%
25.683911511
0.4%
25.47943691
0.4%
25.304524891
0.4%
25.250879841
0.4%
25.228858721
0.4%
25.089643041
0.4%

avg_humidity_2m
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.865614
Minimum20.705882
Maximum77.903972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.347371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.705882
5-th percentile41.295877
Q159.397436
median63.368778
Q365.46644
95-th percentile71.323278
Maximum77.903972
Range57.198089
Interquartile range (IQR)6.0690045

Descriptive statistics

Standard deviation9.0979815
Coefficient of variation (CV)0.14947654
Kurtosis4.7152262
Mean60.865614
Median Absolute Deviation (MAD)2.8174962
Skewness-1.9589075
Sum15338.135
Variance82.773267
MonotonicityNot monotonic
2025-12-07T22:35:04.386695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.561588742
 
0.8%
57.80341882
 
0.8%
65.590749122
 
0.8%
54.711915542
 
0.8%
67.130718951
 
0.4%
58.145299151
 
0.4%
65.521367521
 
0.4%
65.51583711
 
0.4%
64.648064351
 
0.4%
55.740070391
 
0.4%
Other values (238)238
94.4%
ValueCountFrequency (%)
20.705882351
0.4%
24.234791351
0.4%
26.045751631
0.4%
27.732528911
0.4%
29.485168431
0.4%
30.460532931
0.4%
30.777777781
0.4%
32.142785321
0.4%
34.154348921
0.4%
38.737556561
0.4%
ValueCountFrequency (%)
77.903971851
0.4%
76.537456011
0.4%
73.710407241
0.4%
73.496229261
0.4%
73.333333331
0.4%
73.119155351
0.4%
72.383107091
0.4%
72.200100551
0.4%
72.088989441
0.4%
71.97838111
0.4%

avg_wind_speed_10m
Real number (ℝ)

Distinct242
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.021891
Minimum5.639819
Maximum21.916591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.426734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.639819
5-th percentile7.9750503
Q19.8530543
median10.864077
Q312.15132
95-th percentile14.452275
Maximum21.916591
Range16.276772
Interquartile range (IQR)2.2982655

Descriptive statistics

Standard deviation2.0512697
Coefficient of variation (CV)0.18610869
Kurtosis2.6880845
Mean11.021891
Median Absolute Deviation (MAD)1.214002
Skewness0.50925185
Sum2777.5165
Variance4.2077073
MonotonicityNot monotonic
2025-12-07T22:35:04.466645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8834087482
 
0.8%
8.5539467072
 
0.8%
10.685319262
 
0.8%
11.731523382
 
0.8%
10.630266472
 
0.8%
10.960935142
 
0.8%
11.03383612
 
0.8%
13.310658622
 
0.8%
11.648014082
 
0.8%
12.64474612
 
0.8%
Other values (232)232
92.1%
ValueCountFrequency (%)
5.6398190051
0.4%
5.7760683761
0.4%
5.9714932131
0.4%
6.0649572651
0.4%
6.1022121671
0.4%
6.4587732531
0.4%
6.7223227751
0.4%
6.8187028661
0.4%
7.5159376571
0.4%
7.6013574661
0.4%
ValueCountFrequency (%)
21.916591251
0.4%
15.404374061
0.4%
15.257918551
0.4%
15.058974361
0.4%
15.055907491
0.4%
14.959125191
0.4%
14.940723981
0.4%
14.83705381
0.4%
14.752237311
0.4%
14.728205131
0.4%

avg_wind_direction_10m
Real number (ℝ)

High correlation 

Distinct241
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.55818
Minimum78.258421
Maximum269.52539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.509954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78.258421
5-th percentile139.80988
Q1159.47122
median185.04475
Q3203.51898
95-th percentile214.9362
Maximum269.52539
Range191.26697
Interquartile range (IQR)44.047763

Descriptive statistics

Standard deviation26.63101
Coefficient of variation (CV)0.14668031
Kurtosis0.067173446
Mean181.55818
Median Absolute Deviation (MAD)20.575918
Skewness-0.21380879
Sum45752.661
Variance709.21071
MonotonicityNot monotonic
2025-12-07T22:35:04.551137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147.43489192
 
0.8%
164.56812472
 
0.8%
203.67973862
 
0.8%
172.60734042
 
0.8%
153.34841632
 
0.8%
178.00703872
 
0.8%
215.74761192
 
0.8%
145.12921072
 
0.8%
208.94922072
 
0.8%
193.92810462
 
0.8%
Other values (231)232
92.1%
ValueCountFrequency (%)
78.258421321
0.4%
128.04977381
0.4%
130.95977881
0.4%
131.78934141
0.4%
131.84715941
0.4%
134.57214681
0.4%
136.19557571
0.4%
136.68275521
0.4%
137.00301661
0.4%
137.51633991
0.4%
ValueCountFrequency (%)
269.52538961
0.4%
256.81397691
0.4%
229.23479141
0.4%
228.05982911
0.4%
226.14328811
0.4%
221.55153341
0.4%
221.04575161
0.4%
219.33534441
0.4%
218.66616391
0.4%
218.39869281
0.4%

total_precipitation
Real number (ℝ)

High correlation 

Distinct231
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.11111
Minimum3.5
Maximum759.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.590603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile39.63
Q1160.9
median220.05
Q3292.825
95-th percentile440.915
Maximum759.3
Range755.8
Interquartile range (IQR)131.925

Descriptive statistics

Standard deviation130.46477
Coefficient of variation (CV)0.55490689
Kurtosis2.9003386
Mean235.11111
Median Absolute Deviation (MAD)66.55
Skewness1.1729261
Sum59248
Variance17021.057
MonotonicityNot monotonic
2025-12-07T22:35:04.628652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303.43
 
1.2%
229.72
 
0.8%
374.12
 
0.8%
218.72
 
0.8%
160.92
 
0.8%
216.92
 
0.8%
308.82
 
0.8%
239.52
 
0.8%
321.72
 
0.8%
215.22
 
0.8%
Other values (221)231
91.7%
ValueCountFrequency (%)
3.52
0.8%
3.81
0.4%
4.61
0.4%
5.51
0.4%
12.41
0.4%
17.81
0.4%
17.91
0.4%
21.51
0.4%
26.41
0.4%
27.41
0.4%
ValueCountFrequency (%)
759.31
0.4%
755.11
0.4%
727.61
0.4%
677.41
0.4%
656.21
0.4%
638.51
0.4%
5641
0.4%
562.11
0.4%
555.91
0.4%
528.21
0.4%

avg_weather_code
Real number (ℝ)

High correlation 

Distinct241
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1796747
Minimum0.82855706
Maximum21.568125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2025-12-07T22:35:04.671549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.82855706
5-th percentile3.0047511
Q16.5766717
median7.9105078
Q39.389266
95-th percentile13.364505
Maximum21.568125
Range20.739568
Interquartile range (IQR)2.8125943

Descriptive statistics

Standard deviation3.1921846
Coefficient of variation (CV)0.39025814
Kurtosis3.030309
Mean8.1796747
Median Absolute Deviation (MAD)1.4351433
Skewness0.88595579
Sum2061.278
Variance10.190043
MonotonicityNot monotonic
2025-12-07T22:35:04.707715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0915032682
 
0.8%
7.5691302162
 
0.8%
7.6108597292
 
0.8%
11.98692812
 
0.8%
7.6882855712
 
0.8%
5.9406737052
 
0.8%
1.6279537462
 
0.8%
11.661639012
 
0.8%
12.898944192
 
0.8%
7.9069884362
 
0.8%
Other values (231)232
92.1%
ValueCountFrequency (%)
0.82855706391
0.4%
1.2433383611
0.4%
1.3353443941
0.4%
1.565610861
0.4%
1.6279537462
0.8%
1.7843137251
0.4%
1.7888386121
0.4%
1.818501761
0.4%
1.9240824531
0.4%
2.5691302161
0.4%
ValueCountFrequency (%)
21.568124691
0.4%
21.409753651
0.4%
20.449974861
0.4%
18.40321771
0.4%
18.267471091
0.4%
15.100553041
0.4%
14.962292611
0.4%
14.518350931
0.4%
14.367521371
0.4%
14.06787331
0.4%

weather_data_points
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1989
252 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1989
2nd row1989
3rd row1989
4th row1989
5th row1989

Common Values

ValueCountFrequency (%)
1989252
100.0%

Length

2025-12-07T22:35:04.741039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T22:35:04.758751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1989252
100.0%

Most occurring characters

ValueCountFrequency (%)
9504
50.0%
1252
25.0%
8252
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9504
50.0%
1252
25.0%
8252
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9504
50.0%
1252
25.0%
8252
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9504
50.0%
1252
25.0%
8252
25.0%

Interactions

2025-12-07T22:35:03.276585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.622710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.843454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.068407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.308271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.544740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.794365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.038900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.303522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.651117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.867563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.095910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.336771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.576447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-07T22:35:03.066770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.334253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.677552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.895567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.123208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.362542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.606258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.853344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.097394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.457031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-07T22:35:01.923998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.154229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.390673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.640674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.884402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.128780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.484012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.733974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.951097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.184396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.423631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.668987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.913728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.162043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.513370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.760564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.978460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.214179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.453318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.699471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.944630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.191430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.543190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.788531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.008418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.248601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.483811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.729849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.977879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.221022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.570861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:01.818062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.038234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.280021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.516086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:02.760004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.010050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T22:35:03.248742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T22:35:04.773936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_humidity_2mavg_temperature_2mavg_weather_codeavg_wind_direction_10mavg_wind_speed_10mlatitudelongitudetotal_precipitation
avg_humidity_2m1.000-0.0350.737-0.1190.206-0.0070.4510.679
avg_temperature_2m-0.0351.000-0.064-0.768-0.148-0.955-0.2120.311
avg_weather_code0.737-0.0641.000-0.0580.0810.0230.4950.747
avg_wind_direction_10m-0.119-0.768-0.0581.0000.0660.7800.186-0.380
avg_wind_speed_10m0.206-0.1480.0810.0661.0000.1820.139-0.044
latitude-0.007-0.9550.0230.7800.1821.0000.186-0.343
longitude0.451-0.2120.4950.1860.1390.1861.0000.430
total_precipitation0.6790.3110.747-0.380-0.044-0.3430.4301.000

Missing values

2025-12-07T22:35:03.615095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T22:35:03.655036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cfb_schoolnces_nameprocessed_datelatitudelongitudeavg_temperature_2mavg_humidity_2mavg_wind_speed_10mavg_wind_direction_10mtotal_precipitationavg_weather_codeweather_data_points
0Abilene ChristianAbilene Christian University2025-10-24T01:07:19.90330032.468943-99.70979723.44102648.39969813.915686169.708396123.03.9487181989
1Air ForceUnited States Air Force Academy2025-10-24T01:07:19.90330039.010957-104.89135811.91593840.8893929.117999195.71141372.96.1080951989
2AkronUniversity of Akron Main Campus2025-10-24T01:07:19.90330041.078551-81.51167914.64856765.85067912.150679211.036702278.812.2855711989
3AlabamaThe University of Alabama2025-10-24T01:07:19.90330033.211875-87.54597821.83459062.3795889.149271162.788336258.36.2634491989
4Alabama A&MAlabama A & M University2025-10-24T01:07:19.90330034.783368-86.56850219.54208163.39366510.369683185.237808189.56.9889391989
5Alabama StateAlabama State University2025-10-24T01:07:19.90330032.364317-86.29567722.35922661.95927610.264454169.565611244.56.0678731989
6AlbanySUNY at Albany2025-10-24T01:07:19.90330042.685489-73.82466213.05575765.0025149.561639218.666164250.19.4529911989
7Alcorn StateAlcorn State University2025-10-24T01:07:19.90330031.877216-91.14285423.09793962.3609859.930065153.166918199.26.2272501989
8Appalachian StateAppalachian State University2025-10-24T01:07:19.90330036.215536-81.68058313.52860769.23931611.445299218.398693505.512.7204631989
9ArizonaUniversity of Arizona2025-10-24T01:07:19.90330032.232672-110.95081525.25088026.0457527.808396179.48818531.31.9240821989
cfb_schoolnces_nameprocessed_datelatitudelongitudeavg_temperature_2mavg_humidity_2mavg_wind_speed_10mavg_wind_direction_10mtotal_precipitationavg_weather_codeweather_data_points
242Western IllinoisWestern Illinois University2025-10-24T01:07:19.90330040.468086-90.68689915.12996563.93212713.578029199.523379119.86.0316741989
243Western KentuckyWestern Kentucky University2025-10-24T01:07:19.90330036.985035-86.45682818.60799461.73001510.044746176.881850253.27.7933631989
244Western MichiganWestern Michigan University2025-10-24T01:07:19.90330042.282194-85.61375913.81970868.01558612.402262208.396682218.711.4560081989
245Western New MexicoWestern New Mexico University2025-10-24T01:07:19.90330032.776700-108.28328418.32518930.46053311.727702195.68426353.03.3770741989
246William & MaryWilliam & Mary2025-10-24T01:07:19.90330037.269489-76.70821418.05022665.91402710.484113187.042735192.57.6787331989
247WisconsinUniversity of Wisconsin-Madison2025-10-24T01:07:19.90330043.075409-89.40409812.84982466.22171913.512016202.134741117.17.4404221989
248WoffordWofford College2025-10-24T01:07:19.90330034.959411-81.93356818.80829663.7405739.548517153.826043370.58.2171951989
249WyomingUniversity of Wyoming2025-10-24T01:07:19.90330041.311773-105.57931010.58607343.64454513.872750210.84565158.95.5138261989
250YaleYale University2025-10-24T01:07:19.90330041.311158-72.92668815.08909060.65812012.541830203.510307215.17.8406231989
251Youngstown StateYoungstown State University2025-10-24T01:07:19.90330041.104928-80.64659014.56671767.01156411.982303209.701357300.612.5646051989